The Development and Design of a Training Mock-Up for Smart CCTV

Abstract

The rapid advancement of AI-enhanced Smart CCTV systems has created a growing demand for technicians skilled in installation, configuration, operation, and troubleshooting, yet structured hands-on training tools remain limited, particularly in technical-vocational education. This study aimed to develop and validate a Smart CCTV Training Mock-up with accompanying instructional materials for Bachelor of Technology and Livelihood Education (BTLED) students. Employing a developmental research design with a quantitative approach, the study purposively selected 39 fourth-year BTLED Industrial Arts students, five instructors, and a panel of Subject Matter Experts (SMEs). Data were gathered using a Needs Assessment Questionnaire (4-point Likert scale) and an Expert Validation Rubric (5-point Likert scale) and were analyzed using frequency, percentage, mean, and standard deviation. Results revealed that students lacked sufficient hands-on exposure to Smart CCTV systems and considered troubleshooting among the most difficult competencies to master, while all eleven proposed hardware, software, and networking components were rated Very Important (M = 3.67-3.83). Expert validation yielded Very Good to Excellent ratings for the prototype across technical accuracy, instructional design, and safety, and for the instructional materials across content, clarity, and usefulness, with technical accuracy attaining the highest means (M = 4.60). The findings indicate that the developed mock-up is both technically appropriate and educationally effective, offering a validated, cost-effective tool that bridges theoretical instruction and applied competence in intelligent surveillance technology.